Question · asked by 1 review

Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash vs Chlorhexidine mouthwash: Staining index

Population
Adults with dental implants and healthy peri-implant soft tissues at baseline
Intervention
Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash
Comparison
Chlorhexidine mouthwash
Outcome
Staining index

Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash showed no clear difference in staining index compared with chlorhexidine mouthwash; GRADE not reported.

What each review found

Current versions only
No studies found 0 · 0%GRADE not reported 2 · 100%Very low 0 · 0%Low 0 · 0%Moderate 0 · 0%High 0 · 0%
FindingEffectStudiesGRADE certainty
Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash vs Chlorhexidine mouthwash
Staining index · Adults with dental implants and healthy peri-implant soft tissues at baseline · 3 months
Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash showed no clear difference in staining index compared with chlorhexidine mouthwash; GRADE not reported.
Interventions for replacing missing teeth: maintaining and recovering soft tissue health around dental… (2010) · Cochrane All outcomes
MD 0.06 [-0.10, 0.22]1 study · 33 participantsGRADE not reported
Amine fluoride/stannous fluoride (AmF/SnF2) mouthwash vs Chlorhexidine mouthwash
Staining index · Adults with dental implants and healthy peri-implant soft tissues at baseline · 3 months
Interventions for replacing missing teeth: maintaining and recovering soft tissue health around dental… (2008) · Cochrane All outcomesearlier version CD003069.PUB3, superseded
No statistical difference in tooth staining was found at 3 months1 study · 33 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

ReviewSearched up toIncluded studiesAMSTAR 2
Interventions for replacing missing teeth: maintaining and recovering soft tissue health around dental implants
2010 · Cochrane All outcomes
2010-06-02Not listedNot assessed
How reviews are matched to this question

Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.